ANALYZING AND DERIVING GEOGRAPHIC CONTEXTS FOR GENERALIZATION

D. Lee, P.G. Hardy

ESRI, Redlands, USA

dlee@esri.com

The stated aim of many national mapping agencies (NMAs) is to build a master large-scale digital landscape model (DLM), from which medium- or small-scale DLMs are to be derived. The digital cartographic models (DCMs) and subsequent cartographic products are then compiled from the corresponding DLMs. Generalization is at the heart of such a production strategy. To meet the challenge of integrating comprehensive generalization capabilities into ArcGIS (ESRI’s core GIS software product family) to fully support the aims of NMAs requires more research focused on advanced and comprehensive solutions, while the development of fundamental generalization tools continues.

Generalization is about representing the geographic reality as faithfully as possible under map scale restrictions. Although automated tools have been developed to perform specific steps of generalization, such as aggregation of polygons or simplification of lines, it is obvious that post-inspections and corrections would be necessary when putting the individually processed features in context at a target map scale. The increasing demands for contextual generalization have lead to our investigation into typical geographic contexts involved in generalization and into analysis and geoprocessing for deriving information to facilitate contextual generalization.

Geographic features are spatially and semantically related, and interfere with each other in many ways - some are topologically connected, others in relative positions. Geographic patterns - natural subdivisions, cultural areas, clusters, or alignments, can be implicit or explicit. Both model and cartographic generalization share a common principle – they must recognize and preserve these characteristics. It’s easy to find generalization requirements like these two: (1) - “A small building in a rural area should not be excluded if it serves as a landmark”, which would require the determination of the rural area, the neighboring situation of the building within certain extent, and the visibility and significance of the building to travelers; and (2) – “In areas where numerous point features of the same class exist, a representative pattern should be used which will retain the general layout of the features”, this requires measuring of density, recognition of the distribution pattern, and construction of a typified new layout. This paper discusses the various aspects and types of geographical contexts and illustrates the use of geoprocessing models to derive information for contextual generalization. As a parallel task, prototyping of an optimization mechanism for generalization is also in progress. This study and experience in defining and deriving contextual information will be an important input to the optimization process.